Presentation
HyPAS: A Hybrid Optimization Framework for Placement and Sizing Co-Optimization
DescriptionIncremental placement optimization improves power, performance, and area (PPA) by refining cell locations, gate sizing, and buffering under strict physical constraints. Conventional incremental flows, composed of discrete heuristic stages with isolated static timing analyses, often converge slowly and yield inconsistent PPA gains due to the lack of a unified formulation. Recent advances in differentiable placement enable gradient-based refinement on smooth surrogates of wirelength, density, and timing, offering high scalability but limited robustness near legality and timing-closure boundaries. To combine their strengths, we propose HyPAS, a hybrid optimization framework that integrates discrete sign-off optimization with differentiable gradient refinement for placement and sizing co-optimization. The discrete stage, implemented with OpenROAD, ensures legality, timing closure, and buffer-aware repair, while the differentiable stage, built on DREAMPlace, performs gradient-guided placement and sizing using a GNN-based timing-power surrogate. A Straight-Through Estimator bridges continuous gradients with discrete library parameters, enabling end-to-end physical co-optimization. Experiments show HyPAS delivers superior PPA gains with competitive runtime against 2025 ICCAD CAD Contest results and SOTA methods.
Event Type
Research Manuscript
TimeWednesday, July 294:06pm - 4:18pm PDT
LocationMtg Room 202AB